1. SPSS: Using statistical software — a primer
Shang Borui
PhD. Candidate
Department of P.E.

2. Pre-
I am not an expert in neither SPSS nor statistic, SPSS to me is just a research tool;
This workshop is more like informal question-oriented and example-based sharing; you can practice with the examples
Since it’s a primer, the content will be very basic;
Distribute the hand-out of example scale and share the dataset

3. Outline What is SPSS? What I can do with it?
How can I learn the ABCs about SPSS? (some basics like data import and entry)
Optional (If time allows, how can I do inferential statistics in SPSS?)
I will use some examples to illustrate these quesions

4. What is SPSS?
Officially : “Statistical Package for the Social Sciences”
My own thought:
A magic, user-friendly, powerful statistical software!
Easy to be learned but hard to be a master

5. The ABCs about SPSS-Data input
2 basic questions about data entry and import
1) I have the questionnaire result in paper, how can I do the data entry in SPSS (hand-out):
My own procedure can be a reference:
a) Create variables in “Variable View”
b) Define variables column by column
c) Type the data in “Data View”
d) Save it by clicking , save as .sav file
First look at the hand-out, there are totally 7 questions, so we will create 7 variables. I will name the variables one by one. With the first gender, then age, then q1, q2, q3, q4, and q5., done.
Then look at the other columns which you should pay attention to: the type and width column are often useless forget it.
The decimal column, you can make computer know how many decimal digits you want, for example the default number is 2, we go to the data view and enter the age 18, look at the result shows it is Now we switch to the variable view and change it to 0, now go back to data view. It is 18 now.
Then let’s jump into the measure column, there are 3 types of measures, nominal, ordinal, scale. Nominal means only category with no ranking for example, gender, male or female, no ranking; ordinal, category with ranking, it is like grade, year 1, year 2, year 3, freshman, sophermore, junior, senior. Scale variable is continuous variable with meaningful metric, for example time, 3.14 seconds; GPA etc. So in this example the gender should be nominal variable, the age should be scale variable, and the q1 to q5 should be ordinal variable.
In data view, each column represent a variable, and each row represent a participant. For example, here we only got one participant’s data, only one row need to be used. Type all the data.

6. The ABCs about SPSS-Data import
2) I have the data in excel, how can I import them into SPSS (file “GPA IQ example file”)?
a) Toolbar “File→ Import data → Excel”
b) Selecting the options accordingly.

7. How to do the basic descriptive statistics
Descriptive analysis aims to summarize the feature of the data, including:
Frequency (usually for nominal variables);
Minimum, maximum, mean, standard deviation (these usually for scale variables)
Data visualization: bar chart, pie chart;
SD means how disperse or concentrated the data is, low value means the data are concentrated around the mean value, a extreme example can be all of the data with the same value, the SD will be 0. the detailed formula check the textbook

8. Step by step doing descriptive analysis
Frequency and data visualization:
After open the dataset named ”IQ GPA example”:
“ Analyze” (your closest friend in spss) → descriptive statistics→ frequencies → move a nominal variable into the “variable box” → in “charts” select “bar charts” or “pie charts” → anything else keep it default
Click “OK” and see the output window

9. For min, max, mean, SD
“ Analyze” → descriptive statistics→”descriptives” → move a scale variable into the “variable box” → select what you want in “options” → OK
Check the output window

10. More advanced level-inferential statistics in spss
Basic logic of Inferential statistics: Based on probability and hypothesis
We and statistician understand the world with probability and hypothesis but with different terms.
In normal language:
No way! The average height Japanese male is taller than Chinese male.
Rephrase in language of stats:
There will be no significant difference between Japanese and Chinese male.
If the probability is less than 5% (usually), we call it small probability,
small probability=significant

11. How to find the difference between groups of data
Use T-test as an example, Open the file IQ GPA example:
Three variables of IQ, GPA, and Gender.
Hypothesis: There is no significant difference in IQ between male and female
Analyze → Compare means → Independent sample T-test → setting the test variables and group variables → OK
To interpret the result, you need to look at Sig (2-tailed) and t-value
Normally we want Sig as low as possible, vice versa for t-value.

12. How to do the correlation analysis
Use Pearson correlation as an example:
Hypo: No significant correlation between IQ and GPA.
Analyze →Correlate →Bivariate → Drag the two scale variables into the box → Keep it default OK

13. Data Visualization of correlation
Graphs→ Regression plot → setting X and Y axis →Options → Fit linear line →OK

14. Tips
Equip yourself with basic statistical knowledge. Click is easy, but before clicking the mouse, lots of preparation you need to do.
Make full use of Google, Wiki and Youtube
E.g. a very useful material appears when you type
“SPSS for Beginners - Bogdan Kostic” in Youtube
Very useful book in our library named “A handbook of statistical analyses using SPSS”
Although the spss is easy to operate, however, knowing what button you need to click is based on your statistical knowledge
Nearly all kinds

15. My